Machine learning for photonics: from computing to communication

Francesco Da Ros; Ali Cem; Yevhenii Osadchuk; Ognjen Jovanovic; Darko Zibar

doi:10.1109/SUM57928.2023.10224400

Machine learning for photonics: from computing to communication

Francesco Da Ros, Ali Cem, Yevhenii Osadchuk, Ognjen Jovanovic, Darko Zibar

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

Abstract

Neural networks are effective tools for learning direct and inverse models. Here, we review two specific applications of neural networks to photonics: (i) learning accurate direct models for optical matrix multipliers and (ii) inverse modeling for short-reach fiber communication systems, enabling signal equalization.

Original language	English
Title of host publication	Proceedings of 2023 IEEE Photonics Society Summer Topicals Meeting Series
Publisher	IEEE
Publication date	19 Jul 2023
Pages	1-2
Article number	10224400
ISBN (Print)	979-8-3503-4721-0
DOIs	https://doi.org/10.1109/SUM57928.2023.10224400
Publication status	Published - 19 Jul 2023
Event	2023 IEEE Photonics Society Summer Topicals Meeting Series (SUM) - RG Naxos Hotel, Giardini-Naxos, Italy Duration: 17 Jul 2023 → 19 Jul 2023

Conference

Conference	2023 IEEE Photonics Society Summer Topicals Meeting Series (SUM)
Location	RG Naxos Hotel
Country/Territory	Italy
City	Giardini-Naxos
Period	17/07/2023 → 19/07/2023

Keywords

Optical fibers
Inverse problems
Computational modeling
Optical computing
Machine learning
Artificial neural networks
Optical fiber networks

Access to Document

10.1109/SUM57928.2023.10224400

OpenUrl availability

Full text

Cite this

@inproceedings{348422a437df4f3985850a4ad40548de,

title = "Machine learning for photonics: from computing to communication",

abstract = "Neural networks are effective tools for learning direct and inverse models. Here, we review two specific applications of neural networks to photonics: (i) learning accurate direct models for optical matrix multipliers and (ii) inverse modeling for short-reach fiber communication systems, enabling signal equalization.",

keywords = "Optical fibers, Inverse problems, Computational modeling, Optical computing, Machine learning, Artificial neural networks, Optical fiber networks",

author = "Ros, {Francesco Da} and Ali Cem and Yevhenii Osadchuk and Ognjen Jovanovic and Darko Zibar",

year = "2023",

month = jul,

day = "19",

doi = "10.1109/SUM57928.2023.10224400",

language = "English",

isbn = "979-8-3503-4721-0",

pages = "1--2",

booktitle = "Proceedings of 2023 IEEE Photonics Society Summer Topicals Meeting Series",

publisher = "IEEE",

address = "United States",

note = "2023 IEEE Photonics Society Summer Topicals Meeting Series (SUM), SUM 2023 ; Conference date: 17-07-2023 Through 19-07-2023",

}

Ros, FD, Cem, A, Osadchuk, Y, Jovanovic, O & Zibar, D 2023, Machine learning for photonics: from computing to communication. in Proceedings of 2023 IEEE Photonics Society Summer Topicals Meeting Series., 10224400, IEEE, pp. 1-2, 2023 IEEE Photonics Society Summer Topicals Meeting Series (SUM), Giardini-Naxos, Italy, 17/07/2023. https://doi.org/10.1109/SUM57928.2023.10224400

TY - GEN

T1 - Machine learning for photonics: from computing to communication

AU - Ros, Francesco Da

AU - Cem, Ali

AU - Osadchuk, Yevhenii

AU - Jovanovic, Ognjen

AU - Zibar, Darko

PY - 2023/7/19

Y1 - 2023/7/19

N2 - Neural networks are effective tools for learning direct and inverse models. Here, we review two specific applications of neural networks to photonics: (i) learning accurate direct models for optical matrix multipliers and (ii) inverse modeling for short-reach fiber communication systems, enabling signal equalization.

AB - Neural networks are effective tools for learning direct and inverse models. Here, we review two specific applications of neural networks to photonics: (i) learning accurate direct models for optical matrix multipliers and (ii) inverse modeling for short-reach fiber communication systems, enabling signal equalization.

KW - Optical fibers

KW - Inverse problems

KW - Computational modeling

KW - Optical computing

KW - Machine learning

KW - Artificial neural networks

KW - Optical fiber networks

U2 - 10.1109/SUM57928.2023.10224400

DO - 10.1109/SUM57928.2023.10224400

M3 - Article in proceedings

SN - 979-8-3503-4721-0

SP - 1

EP - 2

BT - Proceedings of 2023 IEEE Photonics Society Summer Topicals Meeting Series

PB - IEEE

T2 - 2023 IEEE Photonics Society Summer Topicals Meeting Series (SUM)

Y2 - 17 July 2023 through 19 July 2023

ER -

Machine learning for photonics: from computing to communication

Abstract

Conference

Keywords

Access to Document

OpenUrl availability

Fingerprint

Cite this